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Development of parallel CFD Applications on distributed Memory with Chapel 34

Development of Parallel CFD Applications on Distributed Memo...

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34th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Parenteau, Matthieu Bourgault-Cote, Simon Plante, Frederic Laurendeau, Eric Polytech Montreal Dept Mech Engn Montreal PQ Canada

ISBN: (纸本)9781728174457

Traditionally, Computational Fluid Dynamics (CFD) software uses Message Passing Interface (MPI) to handle the parallelism over distributed memory systems. For a new developer, such as a student or a new employee, the barrier of entry can be high and more training is required for each particular software package, which slows down the research process on actual science. The Chapel programming language offers an interesting alternative for research and development of CFD applications. In this paper, the developments of two CFD applications are presented: the first one as an experiment by re-writing a 2D structured flow solver and the second one as writing from scratch a research 3D unstructured multi-physics simulation software. Details are given on both applications with emphasis on the Chapel features which were used positively in the code design, in particular to improve flexibility and extend to distributed memory. Some performance pitfalls are discussed with solutions to avoid them. The performance of the unstructured software is then studied and compared to a traditional open-source CFD software package programmed in C++ with MPI for communication (SU2). The results show that our Chapel implementation achieves performances similar to other CFD software written in C and C++, thus confirming that Chapel is a viable language for high-performance CFD applications.

关键词： CFD RANS Chapel distributed memory HPC

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ReSLB: Load Balanced Workflow for distributed Deep Learning Mass Spectrometry Database

ReSLB: Load Balanced Workflow for Distributed Deep Learning ...

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IEEE international Conference on High Performance Computing and Communications (HPCC)

作者： Li Mocheng Liu Yang Ou Yang Chen Zhiguang Xiao Nong Chen Tao Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China School of Computer Sun Yat-sen University Guangzhou China State Key Laboratory of Proteomics Beijing Proteome Research Center National Center for Protein Sciences(Beijing) Beijing Institute of Lifeomics Beijing China

The proteomics data analysis pipeline based on the shotgun method requires efficient data processing methods. The parallel algorithm of mass spectrometry database search faces the problems of rapidly expanding database size but low scalability. The heterogeneous database search algorithm based on deep learning is an effective way to solve this problem. Still, the deep learning-based distributed parallel database search algorithm is lacking. This paper analyzes the database searching computational load using a neural network and designs ReSLB, a workflow for the restricted search of mass spectrometry data based on GPU cluster and neural network scoring algorithm. This work aims to ensure the high scalability of future deep learning-based mass spectrometry distributed databases. In the performance estimation of 256 GPUs, the load imbalance of less than 30% and the parallel efficiency of 60% are achieved. Compared with state-of-the-art, the time cost is reduced by 75%, and the parallel efficiency from 1 to 256 GPUs is 3.6x higher than that.

关键词： Deep learning Scalability Neural networks Data preprocessing distributed databases Graphics processing units Clustering algorithms

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Unlocking Personalized Healthcare on Modern CPUs/GPUs: Three-way Gene Interaction Study

Unlocking Personalized Healthcare on Modern CPUs/GPUs: Three...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Diogo Marques Rafael Campos Sergio Santander-Jimé nez Zakhar Matveev Leonel Sousa Aleksandar Ilic INESC-ID Instituto Superior T&#x00E9 cnico Universidade de Lisboa Portugal Polytechnic School University of Extremadura Spain Intel Corporation Russia

Developments in Genome-Wide Association Studies have led to the increasing notion that future healthcare techniques will be personalized to the patient, by relying on genetic tests to determine the risk of developing a disease. To this end, the detection of gene interactions that cause complex diseases constitutes an important application. Similarly to many applications in this field, extensive data sets containing genetic information for a series of patients are used (such as Single-Nucleotide Polymorphisms), leading to high computational complexity and memory utilization, thus constituting a major challenge when targeting high-performance execution in modern computing systems. To close this gap, this work proposes several novel approaches for the detection of three-way gene interactions in modern CPUs and GPUs, making use of different optimizations to fully exploit the target architectures. Crucial insights from the Cache-Aware Roofline Model are used to ensure the suitability of the applications to the computing devices. An extensive study of the architectural features of 13 CPU and GPU devices from all main vendors is also presented, allowing to understand the features relevant to obtain high-performance in this bioinformatics domain. To the best of our knowledge, this study is the first to perform such evaluation for epistasis detection. The proposed approaches are able to surpass the performance of state-of-the-art works in the tested platforms, achieving an average speedup of 3.9× (7.3× on CPUs and 2.8× on GPUs) and maximum speedup of 10.6× on Intel UHD P630 GPU.

关键词： Performance evaluation distributed processing Computational modeling Graphics processing units Genomics Medical services Computer architecture

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An Systematic Study on Blockchain Transaction databases Storage and Optimization 18

An Systematic Study on Blockchain Transaction Databases Stor...

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18th IEEE Int Symp on parallel and distributed Proc with Applicat (ISPA) / 10th IEEE Int Conf on Big Data and Cloud Comp (BDCloud) / IEEE Int Symp on Social Comp and Networking (SocialCom) / IEEE Int Conf on Sustainable Comp and Commun (SustainCom)

作者： Zhang, Jingyu Zhong, Siqi Wang, Jin Wang, Lei Changsha Univ Sci & Technol Sch Comp & Commun Engn Changsha Peoples R China Natl Univ Def Technol Sch Syst Engn Changsha Peoples R China Fujian Univ Technol Sch Informat Sci & Engn Fujian Peoples R China Changsha Univ Sci & Technol Sch Civil Engn Changsha Peoples R China

ISBN: (纸本)9781665414852

As the peer-to-peer networks, blockchain transaction systems require each node to store a complete collection of Unspent Transaction Output (UTXO) in the in-memory transaction database, so as to ensure new transactions can by verified independently and realize the system decentralization. However, the size of blockchain transaction database is growing rapidly, in order to meet the requirements of transaction verification and system decentralization, the nodes need to constantly expand the memory capacity to adapt to the growing database, which leads to the in-memory storage problem of transaction database. This paper analyzes blockchain transaction databases and proposes a storage optimization scheme. The scheme uses the expiration identification method based on Least Recently Used (LRU) algorithm to separate the blockchain addresses into cold and hot zones. It can achieve storage optimization by moving special UTXOs away from the in-memory database, and it can achieve storage optimization without damaging data integrity and security. We present the theoretical analysis for the optimization method to validate the effectiveness. Designed extensive experiments show our proposed method outperforms the current mechanism for the blockchain transaction databases.

关键词： Blockchain system Transaction verification Transaction database

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Trustworthy distributed Deep Neural Network Training in an Edge Device Network

Trustworthy Distributed Deep Neural Network Training in an E...

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IEEE international Conference on Big Data

作者： Sudipta Saha Shubha Haiying Shen Department of Computer Science University of Virginia USA

ISBN: (纸本)9781665480468

With the increased usage of edge devices having local computation capabilities, deep neural network (DNN) training in a network of edge devices becomes promising. Several recent works have proposed fully edge-based distributed training systems for situations when the communication to cloud is unstable or intermittent. However, such distributed systems become vulnerable when there are untrusted devices that launch data and model poisoning attacks during training, deteriorating the accuracy of the DNN model. To handle this challenge, we propose a Trustworthy distributed system for Machine learning training in an edge device network (TrustMe). TrustMe realizes both data and model parallelisms. It detects the untrusted devices producing illegitimate outputs. Next, it reassigns the training tasks of the untrusted devices to other trusted devices in such a way that the reassignment and the training that is restarted after the reassignment require minimal time. Our container-based emulation and real device experiments demonstrate that TrustMe achieves up to 12% higher accuracy and 45% less training time compared to existing methods in the presence of untrusted devices.

关键词： Training Deep learning Image edge detection Neural networks Emulation distributed databases parallel processing

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SPEAR: Dynamic Spatio-Temporal Query Processing over High Velocity Data Streams 37

SPEAR: Dynamic Spatio-Temporal Query Processing over High Ve...

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37th IEEE international Conference on Data Engineering (IEEE ICDE)

作者： Baig, Furqan Teng, Dejun Kong, Jun Wang, Fusheng SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA SUNY Stony Brook Dept Biomed Informat Stony Brook NY 11794 USA Georgia State Univ Dept Comp Sci Dept Math & Stat Atlanta GA 30303 USA

ISBN: (纸本)9781728191843

With the advent of IoT and emerging 5G technology, real-time streaming data are being generated at unprecedented speed and volume having both temporal and spatial dimensions. Effective analysis at such scale and speed require support for dynamically adjusting querying capabilities in real-time. In the spatio-temporal domain, this warrants data as well as query optimization strategies especially for objects with changing motion states. Contemporary spatio-temporal data stream management systems in the distributed domain are mostly dominated by specify-once-apply-continuously query model. Any modification in query state requires query restart limiting system responsiveness and producing outdated or in worst case erroneous results. In this paper, we propose adaptations of principles from streaming databases, spatial data management, and distributed computing to support dynamic spatio-temporal query processing over high-velocity big data streams. We first formulate a set of spatio-temporal data types and functions to seamlessly handle changes in distributed query states. We develop a comprehensive set of streaming spatio-temporal querying methods and propose geohash based dynamic spatial partitioning for effective parallel processing. We implement a prototype on top of Apache Flink, where the in-memory stream processing fits nicely with our spatio-temporal models. Comparative evaluation of our prototype demonstrates the effectiveness of our strategy by maintaining high consistent processing rates for both stationary as well as moving queries over high velocity spatio-temporal big data streams.

关键词： spatio-temporal stream-processing spatiotemporal-stream real-time-spatio-temporal spatial-stream distributed-stream

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Big Data Storage Index Mechanism Based on Hierarchical Indexing and Concurrent Updating

Big Data Storage Index Mechanism Based on Hierarchical Index...

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international symposium on Computer Science and Intelligent Controls (ISCSIC)

作者： Bowen Yan Yang Yang Wei Guo Zhaoguo Zhou Hongwu Wen Zhenyuan Xu Yaobin Wang Army Military Transportation University Tianjin P.R. China Sichuan Institute of Computer Sciences Chengdu P.R. China Southwest University of Science and Technology Mianyang P.R. China

In modern war, it is of great practical significance for the military powers to improve the command efficiency of commanders on the battlefield, it is necessary to use real-time, efficient, and reliable big data storage index method. The application of Hibase-based hierarchical index technology and hot data cache replacement technology can effectively improve the query and storage efficiency of big data, and solve the shortcoming of non-primary key data requiring full table scan. ZooKeeper distributed read-write lock can effectively solve the problem of data consistency caused by frequent concurrent updating of big data information. Finally, the experiment proves that Hive’s query efficiency is improved by nearly 12%.

关键词： Computer science distributed databases Big Data Real-time systems Reliability Intelligent control Indexing

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Live Demonstration: Multi-Laptop HEVC Encoding 52

Live Demonstration: Multi-Laptop HEVC Encoding

引用

IEEE international symposium on Circuits and systems (ISCAS)

作者： Ahovainio, Sami Mercat, Alexandre Vanne, Jarno Tampere Univ Comp Sci Tampere Finland

ISBN: (纸本)9781728133201

This paper presents a demonstration setup for distributed real-time HEVC encoding on a multi-computer system. The demonstrated multi-level parallelization scheme is implemented in the practical Kvazaar open-source HEVC encoder. It allows Kvazaar to exploit parallelism at three levels: 1) Single Instruction Multiple Data (SIMD) optimized coding tools at the data level;2) Wavefront parallel Processing (WPP) and Overlapped Wavefront (OWF) parallelization strategies at the thread level;and 3) distributed slice encoding on multi-computer systems at the process level. This interactive demonstration allows visitors to gradually increase the degree of parallelism in Kvazaar and see the benefits of parallelization in live HEVC encoding. Exploiting all three levels of parallelism on a three-laptop setup speeds up Kvazaar by almost 21x over a non-parallelized single-core implementation of Kvazaar.

关键词： HEVC multi-level parallelization distributed HEVC encoding multi-computer systems

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Machine Learning for CUDA+MPI Design Rules

Machine Learning for CUDA+MPI Design Rules

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Carl Pearson Aurya Javeed Karen Devine Sandia National Laboratories Albuquerque NM USA

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

We present a new strategy for automatically exploring the design space of key CUDA + MPI programs and providing design rules that discriminate slow from fast implementations. In such programs, the order of operations (e.g., G PU kernels, MPI communication) and assignment of operations to resources (e.g., G PU streams) makes the space of possible designs enormous. systems experts have the task of redesigning and reoptimizing these programs to effectively utilize each new platform. This work provides a prototype tool to reduce that burden. In our approach, a directed acyclic graph of CUDA and MPI operations defines the design space for the program. Monte-Carlo tree search discovers regions of the design space that have large impact on the program's performance. A sequence-to-vector transformation defines features for each explored im-plementation, and each implementation is assigned a class label according to its relative performance. A decision tree is trained on the features and labels to produce design rules for each class; these rules can be used by systems experts to guide their implementations. We demonstrate our strategy using a key kernel from scientific computing - sparse-matrix vector multiplication - on a platform with multiple MPI ranks and GPU streams.

关键词： distributed processing Directed acyclic graph Monte Carlo methods Scientific computing Graphics processing units Prototypes Machine learning

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PnyxDB: a Lightweight Leaderless Democratic Byzantine Fault Tolerant Replicated Datastore 39

PnyxDB: a Lightweight Leaderless Democratic Byzantine Fault ...

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39th international symposium on Reliable distributed systems (SRDS)

作者： Bonniot, Loick Neumann, Christoph Taiani, Francois InterDigital Paris France Univ Rennes INRIA CNRS IRISA Rennes France

ISBN: (纸本)9781728176260

Byzantine-Fault-Tolerant (BFT) systems for closed consortia have recently attracted a growing attention notably in financial and supply-chain applications. Unfortunately, most existing solutions suffer from substantial scalability issues, and lack self-governance mechanisms. In this paper, we observe that many workloads present little concurrency, and propose PnyxDB, an eventually-consistent Byzantine Fault Tolerant replicated data-store that exhibits both high scalability and low latency. Our approach hinges on conditional endorsements that track conflicts between transactions. In addition to its high scalability, PnyxDB supports application-level voting, i.e. individual nodes are able to endorse or reject a transaction according to application-defined policies without compromising consistency. We provide a comparison against BFT-SMART and Tendermint, two competitors with different design aims, and show that our implementation speeds up commit latencies by a factor of 11, remaining below 5 seconds in a worldwide geodistributed deployment of 180 nodes.

关键词： distributed databases Fault tolerance

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